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Title: The impact of the platform economy in cities : the case of Airbnb
Author: Shabrina, Zahratu
ISNI:       0000 0004 9352 7038
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2020
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This research contributes to advancing our knowledge on the topical issue of the proliferating use of digital platforms, specifically the home-sharing platform, Airbnb. The aim is to answer the research question of how to measure possible impacts stemming from the adaptation of Airbnb as a form of digital platform economy. A set of various spatial analysis methods and a predictive model were constructed utilising novel datasets such as Airbnb accommodation data and Zoopla rental price data, as well as open government datasets such as dwelling types, housing tenure, and points of interest. This gave rise to a set of methods and results that are four-fold. On the one hand, the spatial distribution and temporal trends, is analysed using the space time cube. These findings show that for London and San Francisco, Airbnb tend to be centrally located, favouring residential areas. In addition, this method also shows the seasonality of the use of the platform. Secondly, using Geographically Weighted Regression (GWR) and the multi-scale form of GWR, it is possible to look at the local scale and the influencing factors for Airbnb locations, and the thesis shows that these are related to functional elements such as hotels, food and beverages availability, and access to public transport links. Thirdly, how Airbnb may be disrupting the housing system by exacerbating the already problematic condition posed by the housing crisis in London is explored. To do this, the focus is shifted onto Airbnb misuse, defined from entire property listings that do not conform with the local regulation, and we look at the relation between those listings and residential areas that are experiencing rapid rental price changes. These changes are measured by extracting the difference in rents for certain years based on the Zoopla longitudinal data. The results conclude that indeed, there is a linear relationship between them, indicating that Airbnb might be putting pressure on housing provision. Lastly, a gravity model is constructed to forecast possible future locations for Airbnb. These are determined in terms of proximity to touristic locations, the historical Airbnb supply, and rental prices. The estimated Airbnb rental distribution based on the model follows a similar distribution to the actual rent derived from Zoopla rental price data. This last outcome suggests that prime Airbnb locations are often located in highly-priced residential neighbourhoods. These often are prime areas for residential location, that now have competing interests with Airbnb conversion. Overall, this thesis provides an analytical perspective that can prompt a conversation on best practices which mitigate the adverse impact of over-saturated short-term rental adaptation in urban settings. Keywords: Platform economy, Airbnb, Zoopla, tourism, housing, gravity model.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available